Load Spatial data
5/12/24
Load libraries
##################
# LOAD LIBRARIES #
##################
library(tidyverse)
library(tidyseurat)
library(Seurat)
library(hdf5r)
# remotes::install_github("czarnewski/niceRplots",force=T)
library(niceRplots)
library(xml2) # loads the image
library(sp)
library(terra)
source("../bin/help_functions.R")
source("../bin/spatial_visualization.R")
#################
# COLOR PALETTS #
#################
pal <- rep(c(RColorBrewer::brewer.pal(9,"Set1"),
RColorBrewer::brewer.pal(9,"Pastel1"),
RColorBrewer::brewer.pal(8,"Accent"),
RColorBrewer::brewer.pal(8,"Set2"),
RColorBrewer::brewer.pal(8,"Pastel2") ,
scales::hue_pal()(8)),99)Load Visium data
#########
# PATHS #
#########
input_dir <- "../data/spatial_data"
result_dir <- "../results/00_load_st_data/"
if( isFALSE(dir.exists(result_dir)) ) { dir.create(result_dir,recursive = TRUE) }
#############
# LODA DATA #
#############
meta <- read_csv("../data/Clinical_data/Clinical_data_Spatial_DMPA.csv")
h5_files <- list.dirs(path = input_dir,
full.names = T, recursive = T) %>%
grep("P\\d\\d\\d$", ., value = TRUE) %>%
set_names(., str_extract(., "P\\d\\d\\d$"))
# h5_files <- h5_files[names(h5_files) %in% sample_id]
# the new version of seurat v5 does not allow to read in any other than low res img
# to circumvent this rename the higres to low res and move the lowres file
image <- map(h5_files,
~Read10X_Image(
filter.matrix = T,
image.dir = paste0(.x, "/spatial"),
image.name = "tissue_lowres_image.png"))
# Read in h5 files and create Seurat Object
seuratObj_list <- pmap(list(h5_files, image, names(h5_files)),
~Load10X_Spatial(
filename = "filtered_feature_bc_matrix.h5",
filter.matrix = T,
assay = "RNA",
data.dir = ..1,
image = ..2,
slice = ..3))
sample_id <- c("P118", "P080", "P031", "P105", "P097", "P108", "P114", "P107") %>% set_names()Tidy up the seurat object
####################################
# RENAME SAMPLES, SPOTS AND IMAGES #
####################################
seuratObj_list <- seuratObj_list %>%
imap(., ~AddMetaData(object = .x,
metadata = rep(.y, length(Idents(.x))),
col.name = "orig.ident")) %>%
map(., ~SetIdent(., value = .@meta.data$orig.ident)) %>%
imap(., ~RenameCells(.x,
new.names = paste0(.y,"_", gsub("-.*","",colnames(.x[["RNA"]])))) ) %>%
imap(., ~ {.x@images <- set_names(.@images,.y); .x})
##################
# MERGE SAMPLES #
#################
# Merge datasets into one single seurat object
DATA <- merge(seuratObj_list[[1]], y = seuratObj_list[2:length(seuratObj_list)])
# DATA <- JoinLayers(DATA) # Seurat version 5.0Load morphology annotation
##########################
# ADD MANUAL ANNOTATION #
#########################
# scale.factors <- map(DATA@images, ~pluck(.x, "scale.factors")$hires)
# img_coord <- map(DATA@images, ~pluck(.x, "coordinates"))
#
# img_coord <- img_coord %>%
# map2(., scale.factors,
# ~mutate(.x, imagecol = .$imagecol * .y,
# imagerow = .$imagerow * .y) )
a2 <- map(sample_id, ~xml2::as_list( read_xml( paste0( input_dir,"/",.x,"/",.x,".svg")) ) )
dd <- map(a2, ~as.numeric( strsplit( attributes(.x$svg)$viewBox , " ")[[1]] ))
dd2 <- map(sample_id, ~dim(DATA@images[[.x]]@image))
# sample_id <- "P097"
# a2 <- a2[["P097"]]
# dd <- dd[["P097"]]
# dd2 <- dd2[["P097"]]
# add image coordinates to the seurat object
get_img_coord <- function(DATA, sample_id){
img_coord <- DATA@images[[sample_id]]@coordinates
DATA@images[[sample_id]]@coordinates <<- img_coord
}
# add manual spatial annotation
get_sp_annot <- function(a2, dd, dd2, sample_id){
scale.factor <- DATA@images[[sample_id]]@scale.factors$hires
img_coord <- DATA@images[[sample_id]]@coordinates
id <- a2$svg %>%
map_chr(., ~attr(.x,"id")) %>%
set_names(seq_along(.), .)
annot_coord <- id %>%
map(., ~get_shape(a2$svg[[.x]]) ) %>%
map(., ~as_tibble(.x, .name_repair="unique")) %>%
# convert the view box from the svg to the pixel dimensions of the raster img:
map(., ~mutate(.x, x = .x[[1]]*dd2[2]/dd[3],
y = .x[[2]]*dd2[1]/dd[4] )) %>%
map(., ~rowid_to_column(., var = "path_idx")) %>%
{. ->> temp} %>%
bind_rows(., .id="name") %>%
group_by(., name) %>%
mutate(elem_idx = cur_group_id()) %>% # group_indices(., name)
ungroup() %>%
mutate(colour = ifelse(grepl("fov|zoom|full_image",.$name),
"transparent", "black")) %>%
list(.) %>%
set_names(., sample_id[1])
DATA@tools <<- append(DATA@tools, annot_coord )
img_coord <- temp %>%
list_modify("fov" = NULL, "full_image" = NULL, "zoom" = NULL) %>%
compact() %>%
imap(., ~mutate(img_coord, !!.y := sp::point.in.polygon(
point.x = img_coord$imagecol*scale.factor,
point.y = img_coord$imagerow*scale.factor,
pol.x = .x$x,
pol.y = .x$y )) ) %>%
map(., ~select(.x, last_col())) %>%
cbind(img_coord, .)
DATA@images[[sample_id]]@coordinates <<- img_coord
sp_annot <- img_coord %>%
rownames_to_column(., var = "barcodes") %>%
mutate(across(7:ncol(.), ~ifelse(. == 0, NA, .)) ) %>%
pivot_longer(., cols = 7:ncol(.), names_to ="sp_annot", values_to = "count") %>%
filter(!(is.na(count))) %>%
group_by(barcodes) %>%
mutate(dupp = row_number()) %>%
ungroup() %>%
filter(., .$dupp == 1) %>%
select(., barcodes, sp_annot)
#DATA <<- left_join( DATA, sp_annot, by=c(".cell"="barcodes", "sp_annot"="sp_annot"))
return(list(coord=annot_coord, annot=sp_annot))
}
annot <- list(a2, dd, dd2, names(a2)) %>%
pmap(., ~get_sp_annot(..1, ..2, ..3, ..4)) %>%
set_names(., names(a2))
sp <- map(annot, 2) %>% bind_rows()
DATA <- left_join( DATA, sp, by=c(".cell"="barcodes"))
DATA# A Seurat-tibble abstraction: 6,700 × 5
# [90mFeatures=36601 | Cells=6700 | Active assay=RNA | Assays=RNA[0m
.cell orig.ident nCount_RNA nFeature_RNA sp_annot
<chr> <chr> <dbl> <int> <chr>
1 P031_AAACGAGACGGTTGAT P031 463 356 SubMuc
2 P031_AAACTGCTGGCTCCAA P031 4081 2232 SubMuc
3 P031_AAAGTAGCATTGCTCA P031 7595 2588 epi
4 P031_AAAGTGTGATTTATCT P031 11394 3873 epi
5 P031_AAAGTTGACTCCCGTA P031 4617 2270 epi
6 P031_AAATACCTATAAGCAT P031 5538 2507 epi
7 P031_AAATCGTGTACCACAA P031 1765 1084 SubMuc
8 P031_AAATGGCCCGTGCCCT P031 789 477 <NA>
9 P031_AAATTAACGGGTAGCT P031 639 465 SubMuc
10 P031_AAATTTGCGGGTGTGG P031 3570 1948 SubMuc
# ℹ 6,690 more rows# adds a matrix with zeros outside the polygon boundary to DATA@misc$alpha
# when plotting it is added as a 4th dim to the image matrix
# to specify alpha for each pixel in the image
mask_background.fun <- function(df, im, sampleid){
# get polygon coordinates for individual polygons:
df <- df %>%
select( any_of(c("name","path_idx", "x", "y"))) %>%
filter(grepl("epi|SubMuc", .$name)) %>%
split(~name)
# Function to create SpatialPolygons from a dataframe
createSpatialPolygons <- function(df) {
# Convert dataframe to SpatialPointsDataFrame
#coordinates(df) <- c("y","x")
coordinates(df) <- c("x", "y")
# Order the points based on 'path_idx'
df <- df[order(df$path_idx, decreasing = F), ]
#df <- df %>% arrange(desc(path_idx))
# Create a SpatialPolygons object
poly <- SpatialPolygons(list(Polygons(list(Polygon(df)), ID = df$name[1])))
return(poly)
}
# Apply the function to each element of the list
polygons_list <- map(df, ~createSpatialPolygons(.x))
# Combine the SpatialPolygons into a single object
combined_polygon <- do.call("rbind", polygons_list)
# Convert it to a polygon (SpatVector)
polygon <- vect(combined_polygon)
# get image matrix and convert to rgb before rasterizing
img <- matrix(
rgb(im[,,1],im[,,2],im[,,3], im[4,,]* 1), nrow=dim(im)[1])
raster <- rast(img)
rasterized <- rasterize(polygon, raster)
rasterized <- flip(rasterized, direction="vertical")
# Plot the polygon
# plot(polygon)
# Crop the raster using the polygon
cropped_raster <- mask(raster, rasterized, updatevalue=0)
# Plot the cropped raster to verify
# plot(cropped_raster)
cropped_raster <- ifel(cropped_raster > 0, 1, cropped_raster)
image_array <- as.array(cropped_raster)[,,1] # keep first dim only
#NA_idx <- which(is.na(image_array[,,1]))
# minpulate image matrix
#im <- replace(im, list = which(image_array != 0)), values = 1)
DATA@misc$alpha <<- append(DATA@misc$alpha, set_names(list(image_array), sampleid) )
}
walk(sample_id, ~mask_background.fun(DATA@tools[[.x]], DATA@images[[.x]]@image, .x) )# can identify the boundary of the tissue and use it to crop
# it gives a more pixelated border than when using a hand drawn polygon as in the function
# above "mask_background.fun()"
find_boundary.fun <- function(im){
img <- matrix(
rgb(im[,,1],im[,,2],im[,,3], im[4,,]* 1), nrow=dim(im)[1])
img <- rast(img)
#plot(img)
i <- im[,,1]+im[,,2]+im[,,3]
#i <- replace(im[,,1], list = which(im[,,1] > .7), values = 0)
raster <- rast(i)
# plot(raster)
raster <- ifel(raster > 2.9, NA, raster)
r <- sieve(raster, directions=8, threshold=500)
# plot(r)
rc2 <- classify(r, matrix(c(0, NA,
2, 1,
3, 1), ncol=2, byrow=TRUE),
include.lowest=TRUE, brackets=F)
plot(rc2)
# Extract only the boundary
bo <- boundaries(rc2, inner=F, classes=T)
# plot(bo)
# Convert SpatRaster to SpatialPolygonsDataFrame
boundary_polygon <- as.polygons(bo)
# plot(boundary_polygon)
# Crop the raster using the polygon
cropped_raster <- mask(img, boundary_polygon, updatevalue=NA)
plot(cropped_raster)
return(boundary_polygon)
}
boundary_polygon <- find_boundary.fun(DATA@images[["P118"]]@image)Add meta data
meta <- meta %>%
select(orig.ident="ID", groups=Contraception) %>%
mutate(groups = ifelse(.$groups=="no HC", "ctrl", .$groups))
DATA <- DATA %>%
mutate(sp_annot2 = .$sp_annot) %>%
mutate(sp_annot = ifelse(grepl("epi", .$sp_annot2), "epi",
ifelse(grepl("SubMuc", .$sp_annot2), "SubMuc", .$sp_annot2 ))) %>%
left_join(., meta, by="orig.ident") %>%
select(groups, sp_annot, everything())# dev.new(height=8, width=5, noRStudioGD = TRUE)
# dev.new(width=7, height=3.5, noRStudioGD = TRUE)
plot_spatial.fun(DATA,
sampleid=sample_id,
sp_annot = F,
alpha = 0,
geneid = "nFeature_RNA",
zoom = "zoom",
ncol = 2,
annot_line = 1,
img_alpha = 1,
point_size = 0) + theme(legend.position = "none") # theme_nothing() #
# dev.new(height=7, width=6, noRStudioGD = TRUE)
# dev.new(height=3.5, width=8, noRStudioGD = TRUE)
plot_st_meta.fun(DATA,
assay="RNA",
feat = "sp_annot",
zoom = "zoom",
ncol = 2,
annot_line = .1,
img_alpha = 0,
point_size = 0.9
)
Identify spots with missing morphology annotation
##########################
# SP MANUAL ANNOTATION #
#########################
keep_epi <- c("P097_CTATTTGCTTGGAGGA", "P031_ATAGAGTTATCAACTT", "P031_GCGGAGAGGGAGAACG", "P031_CCTATCTATATCGGAA", "P031_GGATCTTGACTCAACC","P031_GCCCTAGCCGTCGCGA", "P031_AGCTCTTTACTCAGTT", "P031_AGTCAACACCACCATC", "P031_CGAACCCGCATGCGTC", "P080_TCGAGCCAGGCAGGCC", "P080_AACCCGACAACCCGTG","P080_GGCCGTTTGGGTTTCA","P080_CATTTGAGTGGTACGT", "P080_TTGGTCACACTCGTAA", "P080_CGAACCCGCATGCGTC", "P080_TTGCTGATCATGTTCG", "P080_CGAGACCCTAGAGTGT", "P080_CCAGCCTGGACCAATA", "P080_CGCATGGTGCGATGCT", "P080_ATAGACAACGGGACCT", "P080_CTCATTAACGTTGCCC", "P097_CGAGTTCTGTCCCACC", "P097_GTATGAAATTTCACTC", "P097_AGCAACCGAAAGTAAT", "P097_GAAGCCACTGATTATG", "P097_GCACTGCCTACCTTTA", "P097_CACAGCACCCACGGCA", "P097_AGTTTGGCCAGACCTA", "P097_ACAGAACTGAGAACAA", "P097_GCTTTCAGAGGAGGTG", "P107_ACATCCCGGCCATACG", "P107_ACGCAAACTAATAGAT","P107_ACTTGACTCCCTCTTT", "P107_GATCTTGGAGGGCATA", "P105_AAGACTGCAAGCTACT", "P105_ACACGGGAACTTAGGG", "P105_CACATTCTTTCGATGG", "P105_CCCGACCATAGTCCGC", "P105_CCTATGGGTTACCGTC", "P105_CCTCTAATCTGCCAAG", "P105_CGAGGCTAAATATGGC", "P105_CGTTTCACTTCGGGCG", "P105_GCGCTAATTGAATAGA", "P105_GATATGCGGTAGCCAA", "P105_GGCTCTGCTCCAACGC", "P105_GGGCTGCCTAGGGCGA", "P105_GGCTCTGCTCCAACGC", "P105_GGGCTGCCTAGGGCGA", "P105_GGTTTACAATCTCAAT", "P105_TGTGGCGGGCTTCTGG", "P105_TTAATCAGTACGTCAG", "P105_TTCATGGCGCAACAGG", "P105_TTGGGACACTGCCCGC", "P114_ACCTGCGTGTCATGTT", "P114_CAAGGATCGCATGTTC", "P114_AATGTTGTCGTGAGAC", "P108_ATACGCCGGCGAAACC", "P108_CACGTCGGCAACCTCT", "P108_TCGCCGAAGTTGCGTC", "P108_TTGATTAGCTGTTTCT", "P118_AACCCGACAACCCGTG", "P118_TGAGCCATACAGTCTC", "P118_ATGGGACCTGCTGAAC", "P118_AGCTAACAAGCAATGT", "P118_TTGATTAGCTGTTTCT", "P118_CGTGTCTCGTTACGAC", "P118_GATCAACATAAAGGGA", "P118_TAACAGCGTTTGTGCT", "P118_TGAGCCATACAGTCTC", "P118_GATTACTGAATTTGGG", "P118_TCCAACTTTAAATTCT", "P118_CGATCCTCGCAACATA", "P118_AACCCGACAACCCGTG", "P118_TCTCTTACCGCGAACC", "P118_TATGTAAAGTGCTTAA", "P118_GAAGTTTCCACTCAAT", "P118_TAGTCCCGGAGACCAC", "P118_CGGCCAGAGCGACCAT", "P118_ATAAAGGCTCGGTCGT", "P118_TATTCGTGCCAGAATA", "P118_GGGAGTTAATGAGGCG", "P118_CCGGGCGGTCTCGTCA",
"P118_CTAAATCCGGTGTACA")
keep_SubMuc <- c("P105_TGACATCGAGCGGACC", "P118_GTATCAAACGTTAGCT", "P097_AGGTGGTGACCTTCGC", "P097_AGCCCGGCATTAGAGG", "P031_CTAGTTGGGCCCGGTA","P031_TCTACCGTCCACAAGC", "P031_AAATGGCCCGTGCCCT", "P031_AATCTGGCTTTCTAGT", "P031_AAATGGCCCGTGCCCT", "P031_AATCTGGCTTTCTAGT", "P080_AGGCATTGTCGTAGGG", "P097_GGCAGCAAACCTATGC", "P097_ACAAAGCATGACCTAG", "P097_CCTCCTGTTGTGTCGT", "P097_CGTCGGATAGTGTTGA", "P107_TCTTGATGCGTAGCGA", "P105_ACGATCATCTTGTAAA", "P105_AGATATAATACGACTA", "P105_CTAGGTCTGAAGGAAT", "P114_AAGGGTTTGATTTCAG", "P114_GCACGTGGTTTACTTA", "P114_TAGGCCTATATAGTCT", "P107_TCTTCGATACCAATAA", "P108_GTGCGAAATCGAACAC", "P118_TCGAGACCAACACCGT", "P118_TCGGAGTACATGAGTA", "P118_TATTCAATTCTAATCC", "P118_AGGAGGCCTTCGCGCG",
"P118_ACAATTGTGTCTCTTT", "P118_ACTGAAACGCCGTTAG", "P118_CCCGGGTCGTTCAGGG", "P118_CCCTCCTCGCTCGTAT", "P118_GCGCCTCCCACTCCGA", "P118_GCTAACTGAAGTCTGA",
"P118_TGTGGCGGGCTTCTGG")
###############################
# IDENTIFY MISSING ANNOTATION #
###############################
df <- map(sample_id, ~pluck(DATA@images, .x, "coordinates")) %>%
bind_rows() %>%
#cbind(.,as_tibble(select(DATA, filt))) %>%
cbind(.,as_tibble(select(DATA, orig.ident))) %>%
cbind(.,as_tibble(select(DATA, nCount_RNA))) %>%
cbind(.,as_tibble(select(DATA, nFeature_RNA))) %>%
cbind(.,as_tibble(select(DATA, sp_annot))) %>%
rownames_to_column(var = "barcode") %>%
as_tibble() %>%
select(-starts_with(c("epi_", "SubMuc_")))
f <- df %>%
filter(orig.ident == "P097" & is.na(.$sp_annot)) #%>%
#filter(orig.ident == "P118" & filt == "keep")
# dev.new(width=10, height=10, noRStudioGD = TRUE)
# dev.new(width=5, height=3, noRStudioGD = TRUE)
# DATA %>%
# mutate(sp_annot = case_when(colnames(DATA) %in% keep_epi ~ 'epi',
# colnames(DATA) %in% keep_SubMuc ~ 'SubMuc',
# TRUE ~ .$sp_annot)) %>%
# mutate(filt = case_when(is.na(.$sp_annot) & nCount_RNA < 500 ~ 'filt',
# colnames(DATA) %in% filt ~ 'filt',
# TRUE ~ 'keep')) %>%
# filter(grepl("P097_TAGAATAGCCGATGAA", `.cell`)) %>%
# plot_st_meta.fun(.,
# assay="RNA",
# feat = "filt",
# zoom = "full_image",
# ncol = 2,
# annot_line = .1,
# img_alpha = 0,
# point_size = 1
# )Add missing morphology annotation
# dev.new(width=10, height=10, noRStudioGD = TRUE)
DATA <- DATA %>%
mutate(sp_annot = case_when(colnames(DATA) %in% keep_epi ~ 'epi',
colnames(DATA) %in% keep_SubMuc ~ 'SubMuc',
TRUE ~ .$sp_annot))
DATA %>%
plot_st_meta.fun(.,
assay="RNA",
feat = "sp_annot",
zoom = "zoom",
ncol = 2,
annot_line = .1,
img_alpha = 0,
point_size = 0.8
)
Plot spots to be removed
# dev.new(width=10, height=10, noRStudioGD = TRUE)
DATA <- DATA %>%
mutate(filt = case_when(is.na(.$sp_annot) ~ 'filt',
TRUE ~ 'keep'))
DATA %>%
plot_st_meta.fun(.,
assay="RNA",
feat = "filt",
zoom = "full_image",
ncol = 2,
annot_line = .1,
img_alpha = 0,
point_size = .8
)
Remove NA spots
dim(DATA)[1] 36601 6700# Filter spots outside manual annotation
DATA <- DATA[, !(is.na(DATA$sp_annot))]
DATA$filt <- NULL
dim(DATA)[1] 36601 6612DATA# A Seurat-tibble abstraction: 6,612 × 7
# [90mFeatures=36601 | Cells=6612 | Active assay=RNA | Assays=RNA[0m
.cell groups sp_annot orig.ident nCount_RNA nFeature_RNA sp_annot2
<chr> <chr> <chr> <chr> <dbl> <int> <chr>
1 P031_AAACGAGACG… ctrl SubMuc P031 463 356 SubMuc
2 P031_AAACTGCTGG… ctrl SubMuc P031 4081 2232 SubMuc
3 P031_AAAGTAGCAT… ctrl epi P031 7595 2588 epi
4 P031_AAAGTGTGAT… ctrl epi P031 11394 3873 epi
5 P031_AAAGTTGACT… ctrl epi P031 4617 2270 epi
6 P031_AAATACCTAT… ctrl epi P031 5538 2507 epi
7 P031_AAATCGTGTA… ctrl SubMuc P031 1765 1084 SubMuc
8 P031_AAATGGCCCG… ctrl SubMuc P031 789 477 <NA>
9 P031_AAATTAACGG… ctrl SubMuc P031 639 465 SubMuc
10 P031_AAATTTGCGG… ctrl SubMuc P031 3570 1948 SubMuc
# ℹ 6,602 more rows# dev.new(height=7, width=6, noRStudioGD = TRUE)
# dev.new(height=3.5, width=8, noRStudioGD = TRUE)
DATA %>%
#filter(orig.ident == "P118" | orig.ident == "P097") %>%
plot_st_meta.fun(.,
assay="RNA",
feat = "sp_annot",
zoom = "zoom",
ncol = 2,
annot_line = .1,
annot_col = "black",
img_alpha = 0,
point_size = 0.8
)
Save seurat object
##################################
# SAVE INTERMEDIATE SEURAT OJECT #
##################################
saveRDS(DATA, paste0(result_dir,"seuratObj_merged.RDS"))
# DATA <- readRDS(paste0(result_dir,"seuratObj_merged.RDS")) # seuratObj_merged_seurat_v5.RDSSession info
sessionInfo()R version 4.3.3 (2024-02-29)
Platform: x86_64-apple-darwin20 (64-bit)
Running under: macOS Sonoma 14.4.1
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: Europe/Stockholm
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] RColorBrewer_1.1-3 terra_1.7-71 xml2_1.3.6 niceRplots_0.1.0
[5] hdf5r_1.3.9 Seurat_4.3.0 tidyseurat_0.8.0 SeuratObject_5.0.1
[9] sp_2.1-3 ttservice_0.4.0 lubridate_1.9.3 forcats_1.0.0
[13] stringr_1.5.1 dplyr_1.1.4 purrr_1.0.2 readr_2.1.5
[17] tidyr_1.3.1 tibble_3.2.1 ggplot2_3.5.1 tidyverse_2.0.0
loaded via a namespace (and not attached):
[1] rstudioapi_0.16.0 jsonlite_1.8.8 magrittr_2.0.3
[4] spatstat.utils_3.0-4 farver_2.1.1 rmarkdown_2.26
[7] fs_1.6.4 vctrs_0.6.5 ROCR_1.0-11
[10] spatstat.explore_3.2-6 htmltools_0.5.8.1 sctransform_0.4.1
[13] parallelly_1.37.0 KernSmooth_2.23-22 htmlwidgets_1.6.4
[16] ica_1.0-3 plyr_1.8.9 plotly_4.10.4
[19] zoo_1.8-12 igraph_2.0.2 mime_0.12
[22] lifecycle_1.0.4 pkgconfig_2.0.3 Matrix_1.6-5
[25] R6_2.5.1 fastmap_1.1.1 fitdistrplus_1.1-11
[28] future_1.33.1 shiny_1.8.0 digest_0.6.35
[31] colorspace_2.1-0 patchwork_1.2.0 tensor_1.5
[34] irlba_2.3.5.1 labeling_0.4.3 progressr_0.14.0
[37] fansi_1.0.6 spatstat.sparse_3.0-3 timechange_0.3.0
[40] httr_1.4.7 polyclip_1.10-6 abind_1.4-5
[43] compiler_4.3.3 bit64_4.0.5 withr_3.0.0
[46] MASS_7.3-60.0.1 tools_4.3.3 lmtest_0.9-40
[49] httpuv_1.6.14 future.apply_1.11.1 goftest_1.2-3
[52] glue_1.7.0 nlme_3.1-164 promises_1.2.1
[55] grid_4.3.3 Rtsne_0.17 cluster_2.1.6
[58] reshape2_1.4.4 generics_0.1.3 gtable_0.3.5
[61] spatstat.data_3.0-4 tzdb_0.4.0 data.table_1.15.0
[64] hms_1.1.3 utf8_1.2.4 spatstat.geom_3.2-8
[67] RcppAnnoy_0.0.22 ggrepel_0.9.5 RANN_2.6.1
[70] pillar_1.9.0 vroom_1.6.5 spam_2.10-0
[73] later_1.3.2 splines_4.3.3 lattice_0.22-6
[76] survival_3.5-8 bit_4.0.5 deldir_2.0-2
[79] tidyselect_1.2.1 miniUI_0.1.1.1 pbapply_1.7-2
[82] knitr_1.46 gridExtra_2.3 scattermore_1.2
[85] xfun_0.43 matrixStats_1.2.0 stringi_1.8.3
[88] lazyeval_0.2.2 yaml_2.3.8 evaluate_0.23
[91] codetools_0.2-19 cli_3.6.2 uwot_0.1.16
[94] xtable_1.8-4 reticulate_1.35.0 munsell_0.5.1
[97] Rcpp_1.0.12 globals_0.16.2 spatstat.random_3.2-2
[100] png_0.1-8 parallel_4.3.3 ellipsis_0.3.2
[103] dotCall64_1.1-1 listenv_0.9.1 viridisLite_0.4.2
[106] scales_1.3.0 ggridges_0.5.6 crayon_1.5.2
[109] leiden_0.4.3.1 rlang_1.1.3 cowplot_1.1.3